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Found 10 papers, 4 papers with code
Unsupervised Domain Adaptation Architecture Search with Self-Training for Land Cover Mapping
Thus, we proposed a simple yet effective framework to search for lightweight neural networks automatically for land cover mapping tasks under domain shifts.
ChangeMamba: Remote Sensing Change Detection with Spatio-Temporal State Space Model
For the change decoder, which is available in all three architectures, we propose three spatio-temporal relationship modeling mechanisms, which can be naturally combined with the Mamba architecture and fully utilize its attribute to achieve spatio-temporal interaction of multi-temporal features, thereby obtaining accurate change information.
Ranked #1 on
2D Semantic Segmentation
on xBD
Submeter-level Land Cover Mapping of Japan
Deep learning has shown promising performance in submeter-level mapping tasks; however, the annotation cost of submeter-level imagery remains a challenge, especially when applied on a large scale.
Land-cover change detection using paired OpenStreetMap data and optical high-resolution imagery via object-guided Transformer
Optical high-resolution imagery and OpenStreetMap (OSM) data are two important data sources for land-cover change detection.
Real-Time Semantic Segmentation: A Brief Survey & Comparative Study in Remote Sensing
With the success of efficient deep learning methods (i. e., efficient deep neural networks) for real-time semantic segmentation in computer vision, researchers have adopted these efficient deep neural networks in remote sensing image analysis.
OpenEarthMap: A Benchmark Dataset for Global High-Resolution Land Cover Mapping
We introduce OpenEarthMap, a benchmark dataset, for global high-resolution land cover mapping.
Building Damage Mapping with Self-PositiveUnlabeled Learning
Humanitarian organizations must have fast and reliable data to respond to disasters.
Learning from Multimodal and Multitemporal Earth Observation Data for Building Damage Mapping
In this study, we have developed a global multisensor and multitemporal dataset for building damage mapping.
Dynamic Multi-Task Learning for Face Recognition with Facial Expression
This multi-task learning with dynamic weights also boosts of the performance on the different tasks comparing to the state-of-art methods with single-task learning.
Ranked #1 on
Facial Expression Recognition (FER)
on Oulu-CASIA
FaceLiveNet+: A Holistic Networks For Face Authentication Based On Dynamic Multi-task Convolutional Neural Networks
This paper proposes a holistic multi-task Convolutional Neural Networks (CNNs) with the dynamic weights of the tasks, namely FaceLiveNet+, for face authentication.
